Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Humans can perform parkour by traversing obstacles in a highly dynamic fashion requiring
precise eye-muscle coordination and movement. Getting robots to do the same task requires …

The interest in exploring planetary bodies for scientific investigation and in situ resource
utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art …

Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for
legged robots. We present an end-to-end learned controller that achieves record agility for …

Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …

We introduce a method for generating realistic pedestrian trajectories and full-body
animations that can be controlled to meet user-defined goals. We draw on recent advances …

Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

Parkour is a grand challenge for legged locomotion that requires robots to overcome various
obstacles rapidly in complex environments. Existing methods can generate either diverse …